METAGENOMICS AND BIOLOGICAL ONTOLOGY
*John Dupré
j.a.dupre@ex.ac.uk
Maureen A. O’Malley
m.a.o’malley@ex.ac.uk
* Corresponding author
Mailing address
Egenis, ESRC Centre for Genomics in Society
University of Exeter
Byrne House
St Germans Road
Exeter EX4 4PU
Telephone
+44 (0)1392 269140
Fax
+44 (0)1392 264676
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Keywords
Ontology, metagenomics, metagenome, metaorganism,
multicellularity, system
Abstract
Metagenomics is an emerging microbial systems science that is based on the
large-scale analysis of the DNA of microbial communities in their natural
environments. Studies of metagenomes are revealing the vast scope of
biodiversity in a wide range of environments, as well as new functional capacities
of individual cells and communities, and the complex evolutionary relationships
between them. Our examination of this science focuses on the ontological
implications of these studies of metagenomes and metaorganisms, and what
they mean for common-sense and philosophical understandings of
multicellularity, individuality and organism. We show how metagenomics requires
us to think in different ways about what human beings are and what their relation
is to the microbial world. Metagenomics could also transform the way in which
evolutionary processes are understood, with the most basic relationship between
cells from both similar and different organisms being far more cooperative and
less antagonistic than is widely assumed against a backdrop of ‘nature red in
tooth and claw’. In addition to raising fundamental questions about biological
ontology, metagenomics generates possibilities for powerful technologies
addressed to climate, health and conservation. We conclude with reflections
about process-oriented versus entity-oriented analysis in light of current trends
towards systems approaches.
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METAGENOMICS AND BIOLOGICAL ONTOLOGY
Life is commonly considered to be organized around the pivotal unit of the
individual organism, which is traditionally conceived of as an autonomous cell or
a group of coordinated cells with the same genome.1 Hierarchies of other
biological entities constitute and are constituted by organisms. Macromolecules
are often placed at the bottom of the organism-constituting hierarchy, and they
are succeeded by various sub-cellular and cellular levels of organization,
including the tissues and organs of multicellular organisms. Above the level of
organism rises the organism-constituted hierarchy, in which groups of organisms
form ecological communities across space and lineages or species across time.
Implicit in this hierarchical approach to the organization of life is an evolutionary
timeline that runs from most primitive to most complex. Unicellular organisms are
generally regarded as inhabiting the lower end of the complexity spectrum and
large mammals are placed at the upper end.
All biologists and philosophers of biology know the difficulties of uniquely dividing
different groups of organisms into species or genomes into genes, and both
communities of investigators are divided about the inevitably of pluralism or the
possibility of defining natural kinds. Our view is that these problems reflect a
more fundamental difficulty, that life is in fact a hierarchy of processes (e.g.:
metabolic, developmental, ecological, evolutionary) and that any abstraction of
an ontology of fixed entities must do some violence to this dynamic reality.
Moreover, while the mechanistic models that are constructed on the basis of
these abstracted entities have been extraordinarily valuable in enhancing our
understanding of life processes, we must remain aware of the idealized nature of
such entities, and the limitations of analogies between biological process and
mechanism (we shall say a bit more about this last point in the concluding
section of the paper). Despite the philosophical challenges just mentioned to
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species and gene concepts, there are few similar doubts about the notions of
unicellularity and multicellularity or, indeed, to the prima facie most unproblematic
concept of all, the individual organism. We will raise these questions through a
discussion of an emerging scientific perspective, metagenomics, and conclude
with some further reflections on biological ontology.
Multicellularity
One of the most fundamental divisions in conceptions of the way life is organized
is that between unicellular and multicellular lifeforms. The distinction between
these two modes of life is usually seen as a major evolutionary transition
(Maynard Smith & Szathmáry, 1995). It is standard to understand multicellularity
as the differentiation of ‘monogenomic’ cells (differentiated cells possessing the
same genome) and to think it pertains only to plants, animals and fungi
(excluding yeasts). The most sophisticated forms of multicellularity are usually
attributed to vertebrates because of the complex cell differentiation and
coordination necessary throughout the lifespan of these animals. Nonvertebrates and plants are often considered to be less sophisticated multicellular
organisms, perhaps because many of them can and do ‘revert’ to reproducing
themselves by non-specialized cells. The complexities of incorporating wellknown symbiotic forms such as lichens and ‘Thiodendron’ mats cannot even be
addressed within this scheme. This common way of ranking organisms causes
problems, however, for an adequate understanding of multicellularity.
Many of the characteristics that are used to define multicellularity do not exclude
unicellular life when proper attention is paid to bodies of research that illuminate
cellular cooperation, developmental processes, competition and communication
strategies amongst unicellular organisms (Shapiro & Dworkin, 1997; Shapiro,
1998; Wimpenny, 2000). Less formal criteria for designating unicellularity, such
as visibility, are also problematic, because many supposedly unicellular forms of
life live in groups visible to the naked eye, such as filaments, stromatolites
(microbial mats that bind sediments and are best known in their fossilized forms)
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and biofilms, as well as some very large unicellular organisms. The research we
refer to above reveals that microbial communities do indeed exhibit a multitude of
multicellular characteristics. We have discussed several philosophical aspects of
conceiving of microbial communities as multicellular organisms in earlier work
(O’Malley & Dupré, in press). Here, we will focus on a new genomics-based
strategy called metagenomics that looks at microbial communities with fewer
implicit preconceptions as to what constitutes a single organism.
Metagenomics
Metagenomics – also called environmental genomics, community genomics,
ecogenomics or microbial population genomics – consists of the genome-based
analysis of entire communities of complexly interacting organisms in diverse
ecological contexts. The term ‘metagenome’ was first defined as the collective
genome of the total microbiota of a specific environment by Jo Handelsman and
colleagues in 1998 (Handelsman et al., 1998) and ‘metagenomics’ is now applied
retrospectively to some earlier studies that preceded the coining of the label
(e.g.: Stein et al., 1996). A brief history of where metagenomics fits in both recent
molecular biology and microbiology in general will help clarify its scientific and
philosophical implications.
Historical overview
Microbiology has traditionally been a discipline marginal to the mainstream
biology of macroorganisms.2 Its research interests entered the mainstream of
biological thinking through genetics and molecular biology in the 1940s, when
most molecular and genetic analysis was tied to microorganisms for technical
reasons (Brock, 1990). The use of microbes, especially viruses and bacteria, as
tools to understand genetic inheritance was based on the conviction and
eventual demonstration that microbes possessed not only the same genetic
material that multicellular organisms did, but also many similar biological
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processes – including reproductive ones. As single-gene studies gave way to
whole genome studies in the 1990s, the centrality of microbes and microbial
knowledge to molecular biology was further reinforced, especially as the
sequencing of prokaryotes and viruses rapidly advanced beyond that of
macrobial organisms. Genomic studies of individual isolated microbes, while
adding vast amounts of new information and understanding to comparative
evolutionary biology in particular (Ward & Fraser 2005), has so far provided only
limited knowledge about function and about the many millions of uncultured
microbial taxa (Nelson, 2003).
Attempts to remedy these shortfalls have resulted in the broadening of molecular
microbiology’s environmental scope. Ecological studies of microbes have
historically been peripheral to both general ecology and mainstream
microbiology, the former because of more general tendencies in biology and the
latter because of the predominance of the pure culture paradigm (Brock, 1966;
Costerton, 2004). Although microbial ecology had late nineteenth century roots in
the work of Russian soil microbiologist, Sergei Winogradsky and the founder of
the famous Delft school of microbiology, M. W. Beijerinck, it took until the late
1960s for microbial ecology to gain real disciplinary recognition. Its unifying
theme, whatever the methods used, is that microorganisms have to be
understood in their ecological contexts (which include the context of other
organisms), rather than as isolated individuals in artificial environments (Brock,
1987). Most microbes live preferentially in complex, often multispecies,
communities such as biofilms, and as many as 99% of prokaryote taxa are not
currently culturable in ‘unnatural’ laboratory conditions (Amann et al., 1995).
Taking an environmental approach enabled microbial genomics to extend
beyond the sequencing of laboratory cultures of isolated microorganisms to the
sequencing of DNA extracted directly from natural environments (Pace et al.,
1985; Olsen et al., 1986; Amann et al., 1995). This move out of the laboratory
vastly expanded the scope of the data collected as well as understandings of
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biodiversity and evolutionary relationships (Pace, 1997; Xu, 2006). It took the rest
of the 1990s, however, to overcome another associated narrowness of approach,
in which very limited DNA sequences (often non-protein-coding) were used as
markers of species and indicators of biodiversity. A focus on particular genes and
an assumption they would be the same in each species – whatever the
environment – limited the information gained about the physiological or
ecological characteristics of the organisms (Ward, 2006; Tyson & Banfield, 2005;
Rodríguez-Valera, 2002).
Metagenomics is conceived of as an approach through which such limitations
can be transcended. Instead of individual genomes (‘monogenomes’) or singlegene markers, metagenomics starts with large amounts of the DNA collected
from microbial communities in their natural environments in order to explore
biodiversity, functional interactions and evolutionary relationships. To understand
the full range of metagenomic approaches and the ambitious agenda those
practising it have set for themselves, we will briefly cover the various kinds of
insight generated by the field so far. These range from the generation of
catalogues of biodiversity to projects in evolutionary and functional
metagenomics, and culminate in what we might call ‘metaorganismal’
metagenomics.
1. Biodiversity metagenomics
The primary focus of early metagenomics has been a better appreciation of
biodiversity through the analysis of metagenomes or environmental DNA.
Currently, metagenomicists take two approaches to the study of metagenomes.
The most common practice consists of extracting DNA from environmental
samples and cloning it in large-insert libraries.3 These are screened for clone
activity (particular functions expressed in the host cell) or specific gene
sequences (Riesenfeld et al., 2004). Genes of interest continue to include
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ribosomal RNA genes, genes that have been favoured for phylogenetic analysis
since the early days of molecular microbiology, and which continue to be used as
phylogenetic ‘anchors’ for further analysis of diversity and function (Tringe &
Rubin, 2005). Sampled environments include ocean waters of various depths
and temperatures (DeLong et al., 2006; Grzymski et al., 2006; Béjà, Suzuki, et
al., 2000), marine sediments (Hallam et al., 2004), agricultural soils (Rondon et
al., 2000), the human gut (Manichanh et al., 2006) and mouth (Diaz-Torres et al.,
2003), as well as human-made environments such as drinking-water valves
(Schmeisser et al., 2003). These approaches deliver a mix of targeted and
‘undirected’ biological information about all levels of biological organization, from
single genes and metabolic pathways to ecosystem activities (Riesenfeld et al.,
2004).
The second approach is even more comprehensive and involves the random
shotgun-sequencing of small-insert libraries4 of all the DNA in an environmental
sample. The two ‘classic’ examples include, first, a study that sequenced all the
DNA from an environment with low species density, specifically a biofilm in the
highly acidic metal-rich runoff in a mine that exposed pyrite ore to air and water
(Tyson et al., 2004). The genomes of only five taxa were identified in this
community’s DNA, the relative simplicity of which allowed two individual
genomes to be reconstructed from the sequence data. Analysis of genes for
metabolic pathways gave insights into the roles of community members in
metabolic processes and changed the authors’ understanding of how the
community functioned (Tringe et al., 2005; Tringe & Rubin, 2005).
The second example involved the DNA from a considerably more complex
oceanic community in the low-nutrient Sargasso Sea (Venter et al. 2004). This
catalogue of sub-surface prokaryotic DNA is still the largest genomic dataset for
any community and has been described as a ‘megagenome’ (Handelsman
2004). Almost 70,000 divergent genes and 148 previously unknown phylotypes
(taxa with divergent sequence in commonly used phylogenetic markers) were
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amongst this study’s findings. Special database (GenBank) provisions had to be
made so that the megagenomic data did not overwhelm all the ordinary
monogenomic data contained in the databank and skew subsequent comparative
analyses (Galperin, 2004). Venter’s team continues to gather environmental DNA
from oceans around the world (following the route of The Beagle) and has also
commenced the ‘Air Metagenome Project’, which will sample and sequence the
midtown Manhattan microbial communities in the air (Holden, 2005).
Viral metagenomics, which also focuses on shotgun sequencing of
metagenomes, gives insight into the vast and previously untapped diversity of
viral communities in, for example, near-shore marine environments (sediments
and water column), and human and horse faeces (Breitbart et al., 2002; 2003;
2004; Edwards & Rohwer, 2005). These data, which indicate an even greater
amount of biodiversity than that attributed to prokaryote communities, allow
further hypotheses to be developed about the role of viral communities in the
evolution and ecology of not only microbial communities but the entire natural
world (Hamilton, 2006).
An epistemologically paradoxical but methodologically intriguing use of
metagenomics was to overcome the technical challenges of sequencing ancient
DNA – in the most well-known case from a Pleistocene cave bear, Ursus
spelaeus. By sequencing all the DNA in the sample, which included microbial,
fungi, plant and animal contaminants, then comparing the metasequence against
modern dog and bear sequences, it became possible to distinguish the small
amount (<6%) of cave bear DNA in the sample (Noonan et al., 2005). With
similar techniques applied to mammoth DNA (Poinar et al., 2006), the viability of
ancient DNA sequencing via ‘palaeometagenomics’ now seems established and
may result in the success of the ‘Neanderthal Metagenome Project’ (Rubin, in
Pennisi, 2005). In these cases, the interest is obviously not the metagenome
itself but an individual genome, and ‘metagenomics’ is clearly conceived of as no
more than a bioinformatics technique.
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There is much more to metagenomics than shallow catalogues of sequence and
biodiversity, however. These inventories also give unique insights into microbial
community structure and biogeography. They enable subtle understandings of
ecophysiological characteristics of communities, in which adaptations to different
environmental gradients result in different metabolic and morphological strategies
(e.g. capacities for movement) that spread vertically and horizontally through
community members (DeLong et al., 2006). The genomic heterogeneity in
environmental samples shows that the genomes of single isolated organisms can
no longer be considered as typical of whole populations or species (Allen &
Banfield, 2005). Rich as such understanding is, it is only the beginning of
metagenomic analysis because community genome data is also the basis for
more complex evolutionary understanding as well as for the investigation of
community function and ecological dynamics.
2. Evolutionary metagenomics
Metagenomics has amplified insights into and questions about the genetic
heterogeneity of populations and the genomic mosaicism of individuals.
Understanding this genetic variability requires a deeper understanding of
evolutionary processes and the mechanisms of genetic exchange and
recombination (Falkowski & de Vargas, 2004). Metagenomics naturally aligns
with an area of investigation that is sometimes called ‘horizontal genomics’
because both are concerned with the plethora of mobile genetic elements
available to microbial communities and with the ways in which the metagenomic
resources they inherit are shared and utilized (DeLong, 2004).
The metagenomic role of gene cassettes provides an interesting example of how
such study is being pursued. Gene cassettes are intergenomically mobile genes
that are integrated into genomes in units called integrons. Such cassettes usually
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carry genes for environmental emergencies, such as antibiotic assaults on
prokaryote communities, rather than genes for everyday function. Cassettes, and
the integron elements in the host genome that allow the cassettes to be inserted
and expressed (or excised), are efficient mechanisms for the movement and
expression of genes within and between species, and are implicated heavily in
antibiotic resistance (Michael et al., 2004; Holmes et al., 2003; Rowe-Magnus et
al., 2002). Gene cassettes were originally studied individually but a metagenomic
perspective5 allows them to be treated as a ‘floating’ evolutionary resource of
high diversity and widespread activity that exists independently of individuals and
is likely to have a high impact on bacterial genome evolution (Holmes et al.,
2003; Michael et al,. 2004).
Although the extent, types and precise effects on the metagenome of mobile
resources (cassettes, as well as all the genetic material available for exchange to
greater and lesser degrees by conjugation, transformation and transduction) still
require much more research, the conceptual implications for evolutionary
understanding are already powerful, particularly because such studies back up
extensive work done on lateral gene transfer and recombination processes.
Metagenomic analysis supports and extends the earlier unexpected findings of
comparative microbial genomics, which contradicted the dominant eukaryocentric paradigm of vertical inheritance and mutation-driven species divisions that
give rise to a single tree of life (Allen & Banfield, 2005; Rodríguez-Valera, 2004;
DeLong, 2002b; Doolittle, 2005). Rather than focusing on individual organismal
lineages, such metagenomic studies enable a shift in scientific and philosophical
attention to an overall evolutionary process in which diverse and diversifying
metagenomes underlie the differentiation of interactions within evolving and
diverging ecosystems. Conceptually, metagenomics implies that the communal
gene pool is evolutionarily important and that genetic material can fruitfully be
thought of as the community resource for a superorganism or metaorganism,
rather than the exclusive property of individual organisms (Sonea & Mathieu,
2001).
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3. Functional metagenomics
Most functional metagenomics involves screening library clones created from
environmental DNA for functional activity and specific genes, particularly those
for which function has already been established. Many metagenomic studies
reconstruct metabolic pathways based on genome sequence by assigning
functional roles to different taxa in the community based on those genes (Allen &
Banfield, 2005; Tringe et al., 2005). These analyses are not only discovering new
genes, but also revealing wholly unanticipated functions and mechanisms such
as photobiology in oceanic bacteria. Genes for light-driven energy production
(proteorhodopsin genes) were well known in halophilic or salt-loving archaea but
never before suspected in oceanic bacteria until discovered (because one gene
was serendipitously close to a 16S ribosomal RNA gene being sequenced for
phylogenetic purposes) via metagenomic analysis (DeLong, 2005; Béjà, Suzuki
et al., 2000). Further analysis involved the expression of these genes in a
laboratory E. coli, and the correlation of proteorhodopsin with the light available
at different ocean depths (Béjà et al. 2001; Béjà, Aravind et al. 2000). Venter’s
Sargasso Sea inventory also revealed the surprising abundance of
proteorhodopsin genes in ocean waters.
This gene-based approach is most comprehensively captured by an innovative
comparison of metagenomic data from microbial communities in farm soil and
around the skeletons of decomposed whale carcasses in ocean waters (Tringe et
al., 2005). Using bioinformatic techniques to predict the protein products of each
metagenome, Tringe and colleagues compared their data to the acid mine
drainage and Sargasso Sea metagenomes. They identified patterns of gene
distribution that are specific to the environmental locations of the communities,
thereby adding support to the argument for the importance of understanding
microbial genome activity in situ (even though current techniques are biased
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towards the most common taxa). Rather than trying to reconstruct whole
individual genomes from the metagenomic data, the study sought to elucidate the
‘functional fingerprints’6 of the DNA of complex communities in a variety of
nutrient-rich environments.
Other functional studies of community genomes have uncovered versatile
metabolic capacities,7 such as the theoretically predicted and ecologically
important anaerobic oxidation of methane coupled with nitrate reduction by
microbial consortia from canal mud (Raghoebarsing et al., 2006; Johnston et al.,
2005). Amino acid analysis based on an Antarctic marine bacterial metagenome
has lead to further development of physiological hypotheses about cold
adaptation (Grzymski et al., 2006). Another interesting investigation focused on
‘reverse methanogenesis’ or methane consumption (the reverse of the better
studied process of methane production or methanogenesis) in archaeal
communities8 in deep-sea sediments (Hallam et al., 2004). This study may have
solved a biogeochemical puzzle of why seabed methane does not escape into
the water and is one of the many examples of the potential social and economic
relevance of metagenomics to environmental issues such as global warming.
Many of these studies of function, however, are either of functions predicted from
gene sequence on the basis of homology searches, or are based on lowthroughput screening (Wellington et al., 2003; Johnston et al., 2005). Validating
gene predictions was the bottleneck for monogenomic analysis, and there are
fears this will be the case for metagenomics (Ward, 2006) – especially if
expression studies can only investigate limited gene activity per assay (Sebat et
al., 2003). Several metagenomic research laboratories are now extending their
analyses to the transcriptome (comprehensive gene expression in a particular set
of conditions as measured by the abundance of mRNA transcripts) and proteome
levels (total protein expression). In parallel with the terminology of metagenome,
these objects of investigation are referred to as the metatranscriptome and the
metaproteome. Community microarray analyses of gene transcription are already
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extending bioinformatic inferences of function. Using microarray technology for
studying metagenome expression and regulation is challenging but possible,
according to early efforts (Wu et al., 2001; Dennis et al., 2003; Poretsky et al.,
2005; Zhou, 2003).
Metaproteomic analysis is the next step towards achieving better understanding
of functional gene expression in a range of environments and specific conditions
(Wilmes & Bond, 2006; Powell et al., 2005; Ram et al., 2005; Kan et al., 2005;
Schulze et al., 2005). Using mass spectrometry, Ram and colleagues analysed
the metaproteome of the same acid mine drainage biofilm as Tyson et al.’s
metagenomic study did. Novel gene products as well as expected ones were
quantified, and inferences made from metagenomic data were supported. Most
crucially, a key iron oxidizing enzyme involved in acid production was identified,
which gave much greater depth to understandings of that particular ecosystem.
Metaproteomics is most commonly used for low complexity environments and it
is still very hard to apply to more complex ones or to extend the functional
understanding gained from these early analyses (Wilmes & Bond, 2006).
Nevertheless, existing techniques plus combinations and extensions of them are
being used to understand in situ function, and users are optimistic about further
technical advances (Wilmes & Bond, 2006).
Metametabolomics, the community-based version of individual organism
metabolomics (the study of small molecules or metabolites in a particular
physiological state of a cell) is only just beginning, and one of the first studies
focuses on the metabolic networks of the microbial community in the human gut
and the digestive capacities provided by microbes to humans (Gill et al., 2006).
Such studies also investigate ‘co-metabolomes’ or the metabolites that can only
be produced by host-microbial interactions (Nicholson et al., 2005). As well as
intracellular metabolites, data on extracellular signalling molecules are needed, if
communication and coordination processes within communities are to be
understood (Buckley, 2004). Integrated analyses of metagenomes,
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metatranscriptomes, metaproteomes and metametabolomes will be necessary
for the ultimate metagenomic analysis, which will involve the investigation of all
levels of community activity in situ.
3. Metaorganismal metagenomics, or microbial systems biology
The next ambitiously anticipated phase of metagenomics is microbial systems
science, in which complex biological networks across multiple hierarchical levels
are to be analysed by interdisciplinary teams (DeLong, 2002a; Rodríguez-Valera,
2004; Buckley, 2004). Systems biology more generally is now the most rapidly
proliferating and heavily funded area of biology. It consists of large groups of
molecular and cell biologists, mathematicians and bioinformaticians attempting to
integrate huge bodies of ‘omic’ data with the aim of predicting and intervening in
multiple levels of biological activities in wide-ranging environmental conditions.
Although discussion of how community-based microbial systems biology will be
accomplished has so far been limited, it is clear that a ‘systems ecological’
perspective would meld a molecular approach with a synthesizing perspective on
ecosystems (including biogeochemical analysis) and would thereby extend the
focus of current systems biology on intracellular interactions (DeLong, 2005;
Allen & Banfield, 2005; Doney et al., 2004). More data is not the only requirement
for a systems-biological approach to microbial communities. Advances in
modelling and in silico simulation as well as serious interdisciplinary cooperation
will be vital to the field’s development (Lovley, 2003). These requirements mirror
challenges to systems biology generally (O’Malley and Dupré, 2005).
Despite the limitations of currently available tools,9 a great deal of
metaorganismal analysis is already under way, both of purely microbial systems
and of mixed microbial-macrobial systems. The dynamics between plant and
nitrogen-fixing microbial communities or between squid and light-generating
bacterioplankton are well studied examples of associations in which there are
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mutual influences on gene expression and developmental processes (Xu &
Gordon, 2003). Particularly striking is the growing understanding that symbiotic
bacteria are required for the proper development of many vertebrates. It was
recently reported, for example, that environmentally acquired digestive tract
bacteria in zebrafish regulate the expression of 212 genes (Rawls et al., 2004;
Bates et al., 2006). In fact, for the majority of mammalian organism systems that
interact with the external world—the integumentary (roughly speaking, the skin),
respiratory, excretory, reproductive, immune, endocrine, and circulatory systems,
there is strong evidence for the coevolution of microbial consortia in varying
levels of functional association (McFall-Ngai, 2002). Germfree (gnotobiotic)
rodent studies indicate that removing all or part of a mammalian microbiome
leads not only to abnormal physiological development, but also morphological
abnormalities and immunological depression (Berg, 1996). The roots of plants lie
in the midst of some of the most complex multispecies communities on the
planet. Bacteria and filamentous fungi not only form dense cooperative
communities in the soil surrounding plant roots (often described as the
rhizosphere), but are also found within the roots themselves. The dynamic
interactions – both intra- and extra-cellular – between roots, fungi and bacteria
provide nutrients, control pathogens and structure function, growth and
community composition (Perotto and Bonfante, 1997; Barea et al., 2005). These
interactions, many of them obligate, break down the boundary between plant and
non-plant, and also serve to illustrate that at the heart of every interface between
multicellular eukaryotes and the external environment lies a complex
multispecies microbial community.
Even if we insist on retaining a fundamentally monogenomic conception of the
human organism, there are multiple ways in which studies of macrobe-microbe
interactions can improve our understanding of human biology. A human body is a
symbiotic system composed of 90% prokaryotes and 10% human cells (Savage,
1977) – a ratio that is made more dramatic when viruses (which include the
bacteriophages hosted by every prokaryote) are considered. Humans and their
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microbial partners (the microbiome) form a highly coordinated system that
appears to be the product of coevolution. Microorganismal studies of humanmicrobial interactions conceive microbial communities to be at least as important
for human health as they are for disease. Indeed, it may turn out that diseases
caused by microbial pathogens are best seen not so much as an invasion by a
hostile organism, but rather as a kind of holistic dysfunction of the microbiome.
Analyses of the diversity of the human gut metagenome (e.g.: Gill et al., 2006;
Zoetendel et al., 2006) allow richer understandings of how this many celled
‘organ’ functions and maintains human life and health. Transcriptional studies
further illuminate how this community modulates host gene expression and
communicates with host cells (Hooper et al., 2001). Environmental perturbations
of human microbiomes, such as may be caused by dietary changes or by
antibiotic use, are linked to heart disease, cancers, obesity, asthma and diabetes
(Ordovas & Mooser, 2006; Bäckhed et al., 2004; 2005; Ley et al., 2005; 2006).
Human drug response is highly dependent on molecular interactions with
microbially generated molecules (Nicholson et al., 2005). The human immune
system recognizes a huge variety of prokaryotes organized in different
communities as ‘self’ rather than non-self (the objects of immunological defence).
Together, commensal prokaryote communities and the human host form an
integrated immune system that provides benefits to all the organisms involved
(Kitano & Oda, 2006a, b).
Such ‘self-extending symbioses’, argue Kitano and Oda (2006a), are the
evolutionary norm because they are highly adaptive and robust against
environmental perturbation. All of these insights, whether delivered by
metagenomics or more general molecular microbiology, encourage us to see any
animal as a composite of all three domains (bacteria, archaea10 and eukaryotes)
and our own primary genome as a metagenome of microbial and human DNA
(Gordon et al., 2005). It is even suggested that humans and other animals could
be regarded as ‘advanced fermenters’, the main role of which is to house,
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nourish and assist the reproduction of an enormous array of microbes (Nicholson
et al. 2005). The original human genome sequencing projects were, from this
perspective, about only a tiny and unrepresentative complement of our genes – a
limitation that may ultimately be remedied by a human metagenome project
(Relman & Falkow, 2001).
If we think about symbioses (the fundamental condition of life) even more
generally than those that stop at the outer surfaces of animal bodies, then
metaorganismal metagenomics has very obvious contributions to make to
understandings of ecosystem diversity, function and dynamics. The applications
of microbially grounded systems ecology are extensive. Bioremediation, one of
the great hopes of genetic engineering, applied genetics and microbiology, is
unlikely to be successful without taking a systems-based approach (Cases & de
Lorenzo, 2005). The problem of previous approaches and the reason for their
failures, argue some commentators, is that the genetic engineering of isolated
strains took too limited a perspective on microbial interactions – one that can only
be removed by a multilevel ‘eco-engineering’ approach to the metabolic activities
of indigenous microbial communities in their natural environments. There are
already promising signs from systems-biologic approaches to microbial
bioremediation of environmental pollutants (Pazos et al., 2003).
It is becoming increasingly clear that a range of fundamental questions about life
on this planet will find their answers only with advances in system-based
understandings of microbial communities in global environments (Hunter-Cevera
et al., 2005). Will warming oceans disturb the world’s primary oxygen producers,
the marine cyanobacteria Prochlorococcus and endanger oxygen dependent
lifeforms (everything apart from prokaryotes11)? Will the thawing of the polar
icecaps lead to intensified global warming as dormant methanogenic prokaryotes
become active and release more methane (a contributor to global warming)? Will
global changes in human habitat and diet modify the microbiome in human
bodies and have significant health consequences? Will antibiotics still be
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effective in twenty years or will we see the return of high fatality rates from
infections such as tuberculosis and pneumonia with the worldwide circulation of
antibiotic resistant genes in the microbial metacommunity? All of these are
questions that metagenomics is beginning to address, and it offers real hope that
they will be answered (Doolittle, 2006).
Dynamic system perspectives, which take networks of biological activity in
ecological settings as their objects of study, have some of the most interesting
implications for the philosophy of biology and its traditional considerations about
the units of fundamental ontological importance. In system-level understandings
of microbial communities the metaorganism is conceived of as deriving causal
powers from the interactions of the individual components that constitute it, but at
the same time those components are themselves understood to be controlled
and coordinated in various ways by the causal capacities of the metaorganism.
Although metagenomics might be far away from the full implementation of such a
causally dialectical research programme, every effort to think in systems oriented
ways and find techniques and approaches to answer the questions raised by this
perspective inevitably points to the necessity of integrating multiple levels of
separately realized biological insight. As mentioned earlier, this is one of the
main challenges being addressed by systems biology generally.
Concepts of metagenome and metaorganism
For many of the field’s practitioners, metagenomics is a technique that provides
access to otherwise inaccessible microbial communities. We believe that a more
complex line of thought informs metagenomics and has probably informed it
since its inception (e.g.: Rodríguez-Valera, 2004). This perspective takes very
seriously the proposal that metagenomes are communal resources and that the
entity to which the resource is available is a coordinated, developing
multifunctional multicellular organism composed of large numbers of cells of
20
different varieties and capabilities, able to work in ways in which the collectivity
regulates the functions of individuals12. Individual organisms, from this viewpoint,
are an abstraction from a much more fundamental entity. A lot of evidence for
this perspective has been generated by a variety of methods over the last two
decades (e.g.: Shapiro, 1997; Kolenbrander, 2000; Kaiser, 2001). Metagenomic
analysis is able to take these avenues of research into account and begin to treat
the metagenome as the concept that most effectively captures the ways in which
microbes survive and flourish in such a remarkable range of diverse
environments.
Ultimately, metagenomics is about ‘the community of all communities’ on this
planet, and, uncomfortable as the notion may be for many philosophers, for some
practitioners metagenomics is perceived to be moving us inexorably in the
direction of a Gaia-like concept of the world (Doolittle, 2006). Taking the
interconnectedness of ecosystems into account does not mean taking on board
all Lovelock’s philosophical notions or empirical predictions (going back to Baas
Becking, who first applied the word in 1931, is probably a better idea13), but it
does mean that adequate understandings of climate change, global human
health and international economic prosperity will all depend on metagenomic
knowledge.
Competition versus cooperation
It is plausible to conclude that the fundamental activity of cells, beyond selforganization and maintenance, is to form cooperative associations in a plurality
of forms. This may suggest that the organization of life is determined not by
competition but by the ability to cooperate (albeit competitively). Or put another
way, though there is little doubt that competition and selection have been
essential to the evolutionary process, it may be that the main respect in which
organisms (or cells) have competed has been with respect to their ability to
cooperate in complex single- or multi-species communities. If this is the case,
then we might expect – contrary to the orthodox evolutionary view that sees
21
altruism as exceptional and requiring special explanation14 – the ubiquity of
organisms disposed to act for the benefit of other organisms or cells. Darwin was
right about the general picture of the organic environment being fundamental to
the determination of fitness, but his account of the relationships between
organisms in those environments needs to be supplemented.
Process versus entities
A final crucial point about metaorganisms is that they are paradigmatically
dynamic entities and, therefore, very clear illustrations of the ultimate necessity of
a process-oriented approach to biological investigation. None of the entities that
constitute organisms or which organisms constitute is static. Genomes, cells, and
ecosystems are in constant interactive flux, subtly different in each iteration, but
similar enough to constitute a distinctive process. The greatest importance of this
point is perhaps that its appreciation will prevent us from taking too literally
mechanistic models of biological processes. A good machine starts with all its
parts precisely constructed to interact together in the way that will generate its
intended functions. The technical manual for a car specifies exactly the ideal
state of every single component. Though the parts of a machine are not
unchanging, of course, their changes constitute a relentless and one directional
trend towards failure. As friction, corrosion, and so on gradually transform these
components from their ideal forms, the function of the car deteriorates. For a
while these failing components can be replaced with replicas, close to the ideal
types specified in the manual, but eventually too many parts will have deviated
too far from this ideal, and the car will be abandoned, crushed, and recycled. No
such unidirectional tendency to failure characterizes biological processes.
Though perhaps all organisms die in the end, many exhibit high levels of stability
for centuries and millennia. This is not, however, a stability based on parts so
durable that they take centuries to deteriorate, but the dynamic stability of
processes that constantly recreate or maintain their essential constituents. Both
the promise and the challenge of systems-biologic approaches is that they offer
22
the hope of providing techniques for the modelling of such self-maintaining
dynamic systems.
A further philosophical point about dynamically self-sustaining systems as
opposed to mechanistic15 ones is that in the latter causation can be seen to run
not merely upwards from part to whole, but also downwards from whole to part16.
The behaviour of individual cells, for instance, whether in multicellular eukaryotes
or complex microbial systems, is in fundamental respects determined by the
features of the system of which it is part. If we are successful in producing useful
object-like abstractions from the flux of dynamic biological process this reflects
the fact, we suggest, that these ‘objects’ are temporarily stable nexuses in the
flow of upward and downward causal interaction. So, for instance, a gene is a
part of the genome that is a target for external (i.e.: cellular) manipulation of
genome behaviour and, at the same time, carries resources through which the
genome can influence processes in the cell more broadly. Because the analysis
of biological systems into entities is not determinate, then for some purposes of
inquiry a monogenomic organism is the most appropriate, whereas for others, the
appropriate focus is on polygenomic systems. Answering the question, ‘What is
an organism?’, requires seeing that there are is a great variety of ways in which
cells, sometimes genomically homogeneous, sometimes not, combine to form
integrated biological wholes. The concept of multicellular organism is, therefore,
a far more complex and diverse one than the simple straightforward category we
outlined in the introduction.
Conclusion
A specific and central philosophical message of this paper is to encourage
scepticism about a concept that has often been treated as self-evident and
unproblematic in theoretical biology: the monogenomic concept of an organism.17
Its ‘obviousness’ is sustained by the distinction between unicellularity and
23
multicellularity, where it is assumed that each organism has exactly one genome.
If that genome appears in sharply differentiated but functionally interrelated cells
we think of the organism as multicellular; if cells with that genome seem more or
less functionally homogeneous and more or less contingently functionally
interrelated to other cells, we call it unicellular.
However, if we think of the organism as whatever cooperative systems of cells
are most usefully recognized for exploring biological function, the assumption of
‘one genome, one organism’ starts to look like a poorly grounded dogma. As
highly organized microbial communities such as biofilms illustrate, genomically
diverse groups of cells can form organism-like communities (Stoodley et al.,
2002; Costerton et al., 1995; O’Toole et al., 2000). Moreover, to understand the
functions of even such paradigmatically multicellular organisms as ourselves,
from many perspectives — nutrition, development, immune response, among
others — it turns out that the relevant system is actually a much broader one that
includes many (genomically) quite different kinds of cells. Indeed, as we have
noted, the majority of cells in the systems we think of as humans are actually
microbial rather than what we conventionally think of as human cells. No doubt
what we are describing as a dogma is seen by many biologists as firmly
grounded in a conception of evolution as occurring in genetically isolated
lineages. But as we have also argued (and in more detail elsewhere [O’Malley &
Dupré, in press]) this view of evolution is itself based on a perspective that is
dangerously macrobocentric. The prevalence of horizontal genetic transfer
makes it seem increasingly implausible to see the evolution of microbes as fitting
within this model, and indeed suggests that evolving units may turn out quite
typically to be multispecies communities. Proper attention to microbes may force
us to rethink some fundamental ideas about the evolutionary process.
Philosophers of biology have for many years raised concerns about central
ontological categories, most notably the species and, more recently, the gene.
We are urging that the organism join this group of problematic biological
24
categories. But we also propose here a diagnosis of these ontological problems.
Analysing biological processes into things is necessarily an abstraction. Life is
not composed in a machine-like way out of unchanging individual constituents.
Genomes, cells, organisms, lineages are all assemblages of constantly changing
entities in constant flux. The stability of life processes is not maintained (as in a
mechanism) by the constant interactions of unchanging parts, but by dynamic,
self-sustaining and self-repairing processes. There is no doubt that mechanistic
investigations of life processes have provided profound insights, and this fact is
enough to show that quasi-mechanistic elements are fundamental constituents of
life processes. But there are limits to how far mechanistic investigations can take
us in understanding the dynamic stability of processes at this hierarchy of
different levels. Such understanding will require models that incorporate both the
capacities provided by mechanistic or quasi-mechanistic constituents, and the
constraints and causal influences provided by properties of the wider systems of
which these constituents are parts. It remains to be seen how far we have the
abilities to construct such models, but this is what enthusiasts for the rapidly
growing project of dynamic metaorganismal metagenomics and systems biology
should be aiming to offer.
APPENDIX
Problems and limitations of metagenomic analysis
At present, metagenomics is in an intensive discovery phase not unlike that of
early single-organism genomics. It is more concerned with constructing
inventories of environmental sequence and gene products than ready to give
extensive insight into ecosystem function and physiology (Schloss &
Handelsman, 2003; Chen & Pachter, 2005). Even if inventories were the only
25
aim, the full metagenome sequence of the most complex and diverse
communities (especially in soils) is still beyond the reach of current technologies
because of the size and complexity of soil-based communal genomes, which
require formidably high numbers of clones and sequence coverage to accurately
represent their genetic composition (Riesenfeld et al., 2004). Shallower
sequencing coverage means lower representation of less common taxa and
functions (Foerstner et al., 2006). Various technical biases in regard to sampling,
lysing cells for DNA extraction, cloning and expression systems are all being
worked around and may eventually be overcome (Handelsman et al. 2002; Streit
& Schmitz, 2004; Liles et al., 2003; Béjà, 2004).
There are additional sources of bias imposed by technological limitations. The
extension of tools devised originally for individual genome and proteome analysis
is not unproblematic. Reconstructing individual genomes from shotgunned
metagenomes (which would accord the latter more reliability) is very difficult,
especially in complex communities with a lot of genomic microheterogeneity
(DeLong, 2005). New assembly algorithms and more extensive sequencing
coverage are amongst the potential solutions (Edwards & Rohwer, 2005; Allen &
Banfield, 2005; Chen & Pachter, 2005), as well as combinations of small-insert
and large-insert library construction (DeLong, 2005). Comparisons of
metagenomic libraries with monogenomic data are still crude, because of the low
diversity of monogenomic data and (possibly) inadequate search tools. While
comparative metagenomics, or the comparison of the genomic diversity and
activity of different microbial communities, has already begun (Tringe et al.,
2005) it is currently shallow. Likewise, assigning functional roles to large
numbers of environmental proteins is still impossible, which restricts
understanding of metabolic activity in communities (Allen & Banfield, 2005).
Metagenomic expression methods developed so far analyse only bacterial gene
expression because they are restricted to E. coli based techniques (de Lorenzo,
2005). Many metagenomic studies are more ‘proof of principle’ than wholly
successful extensions of existing tools but the potential rewards for overcoming
26
such challenges are considered to provide more than enough motivation for
investing time and effort in their development (Chen & Pachter, 2005; Nicholson
et al., 2004).
System-oriented analyses of the complex functions of microbial communities in
their environments is still rudimentary (DeLong, 2005; Ram et al., 2005;
Handelsman, 2004; Torsvik & Øvreås, 2002). Advances in the integration of
‘omic’ data modeling techniques for single-organism lab-based monogenomic
systems biology (Joyce & Palsson, 2006; Palsson, 2000) are highly likely to
benefit metaorganismal metagenomic analyses. The development of a systemsbased molecular approach to microbial communities will also require the tighter
integration of microbial ecology, population genomics, phylogeny and
biogeochemistry (DeLong, 2005; Rodríguez-Valera, 2002). More hypothesisdriven approaches (combining, for example, functional and sequencing aspects
of metagenomics in the same investigation) and supplementary data from
traditional microbiological techniques (e.g.: culturing, microscopy and other
visualization techniques) are seen as desirable (Schloss & Handelsman 2005;
Ward, 2006). If metagenomic-based systems biology progresses even a little, it
will revolutionize the way in which life is investigated.
ACKNOWLEDGEMENTS
We thank the participants at the Philosophical and Social Dimensions of
Microbiology Workshop (University of Exeter, July 2007) for discussion and
ideas, and our two referees for helpful comments on the paper. We gratefully
acknowledge research funding from the UK Arts and Humanities Research
Council, and workshop funding from the Wellcome Trust. John Dupré did part of
the work for this paper while holding the Spinoza chair in Philosophy at the
University of Amsterdam and would like to thank the Department of Philosophy
for hospitality and support. The research in this paper is part of the programme of
27
the Economic and Social Research Council’s Centre for Genomics in Society
(Egenis).
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1
If viruses (which are included in the usual definitions of microbes) are given
organismal status, then an appropriate definition of organism could not insist on
cellularity. Although there is a strong case for defining life as fundamentally
cellular (most biologists would agree on this), the exclusion of viruses from the
category of organisms raises deep conceptual problems due to the increasing
recognition of the centrality of viruses to life processes at all scales.
2
In O’Malley and Dupré (in press) we suggest the word ‘macrobe’, in contrast to
‘microbe’, to refer to the diverse group of eukaryotic organisms generally thought
of as multicellular.
44
3
DNA fragments of 100 kb and more can be propagated in bacterial artificial
chromosomes (BACs) and up to 40 kb in fosmids (modified plasmids). These
cloning vectors were originally used primarily for plant and animal genomics, and
BACs were essential to the sequencing of the human genome.
4
These libraries consist of very small fragments of DNA cloned in plasmids and
amenable to high-throughput sequencing.
5
The metagenomic technique is somewhat different for gene cassettes because
in this case PCR assays that target complete ORFs (specifically those flanked by
the integration elements that are part of integrons) can be used to select
environmental DNA prior to cloning and sequencing (see Stokes et al., 2001).
6
Tringe and colleagues used partially assembled metagenome fragments, which
they called ‘enviromental gene tags’ or EGTs.
7
Biotechnological hopes for applications arising from functional metagenomics
are high, because the science opens up a previously concealed realm of
microbial gene products (apparently inaccessible to standard cultivation
techniques). Commercial applications of metagenomics focus on the discovery of
‘novel natural products’ such as enzymes, antibiotics and other drugs (Cowan et
al., 2005; Lorenz & Eck, 2005; Li & Qin, 2005; Courtois et al,. 2003). Soils in
particular are perceived to be rich sources of new biomolecules for industry and
biomedicine (Daniel, 2004; Voget et al., 2003). In cases such as acid mine
drainage, metagenomic data is anticipated to lead to more effective
bioremediation techniques (Eyers et al., 2004; Pazos et al., 2003). Even broader
insight and applications are sought from metagenomics to address global
warming (Doolittle, 2006).
8
These communities were physically simplified before metagenomic analysis (to
a greater extent than size filters during sampling would achieve), so are not truly
‘natural’ samples.
9
See Appendix: Problems and limitations of metagenomic analysis
10
Much less is known about archaeal symbionts than bacterial ones, but
numerous recent studies are investigating their presence and role in all
45
organisms, including humans. Methanogens (methane generating archaea) are
the most frequently found symbionts, in host environments such as the human
gut and mouth (Lange et al., 2005). There are still no known pathogenic archaea,
but the limitations of detection tools may be the most appropriate interpretation of
this non-finding (Eckberg et al., 2003).
11
It appears unlikely that there are any truly obligate anaerobic eukaryotes
(Bryant, 1991; Lloyd, 2004).
12
There are, of course, some microbiologists who explicitly reject this
perspective and argue for the retention of a single-organism focus, even if
metagenomics has proved its use as a tool (e.g.: Buckley, 2004).
13
See Baas Becking (1931) as well as Lynn Margulis’ work on this topic (e.g.:
Margulis, 1998).
14
Altruism and its interpretation have been much deliberated in the biological
literature. See Lyon (this issue) for a discussion of altruism and cooperation in
relation to microbes.
15
Or at any rate machine-like ones. The concept of a mechanism developed
recently by some philosophers (Machamer et al., 2000; Craver 2005) is in many
ways well-suited to the representation of dynamic biological systems. We do
question the appropriateness of the term ‘mechanism’ to such systems, however.
16
Craver and Bechtel (in press) provide an interesting but rather different
perspective on top-down causation in complex systems from that assumed here.
17
The concept is not wholly monolithic because of known exceptions, such as
genomic mosaicism.